Import packages¶
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import scipy.stats as stats
import math
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.impute import KNNImputer
from fancyimpute import KNN
from functools import reduce
import plotly.express as px
import plotly.graph_objects as go
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
import plotly.io as pio
pio.renderers.default='notebook'
from sklearn.metrics import (confusion_matrix,
accuracy_score)
import warnings # For handling error messages.
# Don't worry about the following two instructions: they just suppress warnings that could occur later.
warnings.simplefilter(action="ignore", category=FutureWarning)
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")
# We import the dataset that we created in our Data Wrangling section of the Capstone project
# Import the dataset
df=pd.read_csv('/Users/amrita/Desktop/dummies_data.csv')
df.head()
# We start by creating visualization of the target variable
# Our plots suggest that our classes are balanced
f,ax=plt.subplots(1,2,figsize=(18,8))
df['satisfaction'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',ax=ax[0],shadow=True)
ax[0].set_title('satisfaction')
ax[0].set_ylabel('')
sns.countplot('satisfaction',data=df,ax=ax[1])
ax[1].set_title('satisfaction')
plt.show()
54.7% people are satsfied with the Airlines and 45.3% are not satisfied with the Airlines.
df
Age and Satisfaction¶
def generate_group(column_name,kind):
group = df[[column_name,'Age']]
group = group.groupby([column_name]).mean()
if kind == 'bar':
fig = px.bar(group,title = f"<b>Average age by {column_name}</b>")
fig.show()
return group
age = generate_group('satisfaction',kind = 'bar')
Most satisfied customers lie in the age group of 41 years approx and most dissatisfied customers lie in the age group of 38 years (approx)
Flight Distance and Satisfaction¶
def generate_group1(column_name,kind):
group1 = df[[column_name,'Flight_Distance']]
group1 = group1.groupby([column_name]).mean()
if kind == 'bar':
fig = px.bar(group1,title = f"<b>Average Flight_Distance by {column_name}</b>")
fig.show()
return group1
Flight_Distance = generate_group1('satisfaction',kind = 'bar')
Most satisfied customers flew a flight distance of 1944.47 miles with the airlines and dissatisfied customers flew around 2025.203 miles with the airlines.
Departure Delay and Satisfaction¶
def generate_group3(column_name,kind):
group3 = df[[column_name,'Departure_Delay_in_Minutes']]
group3 = group3.groupby([column_name]).mean()
if kind == 'bar':
fig = px.bar(group3,title = f"<b>Average Departure Delay in Minutes by {column_name}</b>")
fig.show()
return group3
Departure_Delay = generate_group3('satisfaction',kind = 'bar')
Most dissatisfied customers had an average delay of 18 minutes (approx) and dissatisfied customers flew around 2025.203 miles with the airlines.
Arrival Delay and Satisfaction¶
def generate_group4(column_name,kind):
group4 = df[[column_name,'Arrival_Delay_in_Minutes']]
group4 = group4.groupby([column_name]).mean()
if kind == 'bar':
fig = px.bar(group4,title = f"<b>Average Arrival Delay by {column_name}</b>")
fig.show()
return group4
Arrival_Delay = generate_group4('satisfaction',kind = 'bar')
# Gender and Flight Satisfaction
f,ax=plt.subplots(1,2,figsize=(18,8))
df[['Male','satisfaction']].groupby(['Male']).mean().plot.bar(ax=ax[0])
sns.countplot('Male',hue='satisfaction',data=df,ax=ax[1])
ax[1].set_title('gender: Satisfied vs Not Satisfied')
ax[0].set_title('satisfaction vs gender')
plt.show()
1st plot: Females are more satisfied than males. 2nd plot: Females are more satisfied with the airlines. Males are more dissatisfied with the airlines
# Disloyal Customer and Flight Satisfaction
f,ax=plt.subplots(1,2,figsize=(18,8))
df[['disloyal_Customer','satisfaction']].groupby(['disloyal_Customer']).mean().plot.bar(ax=ax[0])
sns.countplot('disloyal_Customer',hue='satisfaction',data=df,ax=ax[1])
ax[1].set_title('disloyal_Customer: Satisfied vs Not Satisfied')
ax[0].set_title('satisfaction vs disloyal_Customer')
plt.show()
1st plot: Disloyal customers of the airlines are less satisfied compared to loyal customer 2nd plot: Loyal customers of the airlines are more satisfied than not satisfied; Disloyal customers are more dissatisfied than satisfied.
# Travel type vs Flight Satisfaction
f,ax=plt.subplots(1,2,figsize=(18,8))
df[['Personal_Travel','satisfaction']].groupby(['Personal_Travel']).mean().plot.bar(ax=ax[0])
sns.countplot('Personal_Travel',hue='satisfaction',data=df,ax=ax[1])
ax[1].set_title('Personal_Travel: Satisfied vs Not Satisfied')
ax[0].set_title('satisfaction vs Personal_Travel')
plt.show()
1st plot: Customers doing business travel are more satisfied compared to personal travel customers 2nd plot: Customers doing business travel are more satisfied than not satisfied; customers doing personal travel are more dissatisfied than satisfied.
pd.crosstab(df.Cleanliness,df.satisfaction, margins=True).style.background_gradient(cmap='summer_r')
A cleanliness level of 4 and 5 yields higher satsfaction among customers.
df.columns
pd.crosstab(df.Seat_comfort,df.satisfaction, margins=True).style.background_gradient(cmap='summer_r')
A seat comfort level of 4 and 5 yields higher satsfaction among customers.
pd.crosstab(df.Inflight_entertainment,df.satisfaction, margins=True).style.background_gradient(cmap='summer_r')
An Inflight_entertainment level of 4 and 5 yields higher satsfaction among customers suggesting better inflight entertainment provides higher satisfaction.
pd.crosstab(df.Ease_of_Online_booking,df.satisfaction, margins=True).style.background_gradient(cmap='summer_r')
A better ease_of_online_booking service will yield more satisfaction among customers as suggested by the ratings of 4 and 5 for ease_of_Online_booking service.
pd.crosstab(df.Inflight_wifi_service,df.satisfaction, margins=True).style.background_gradient(cmap='summer_r')
Inflight wifi service doesn't hold much importance for satisfaction because we can see that even if inflight wifi service has a got a rating of 2, the number of customers satisfied is more than no of customers dissatisfied
pd.crosstab(df.On_board_service,df.satisfaction, margins=True).style.background_gradient(cmap='summer_r')
Better onboard service will yield better satisfaction among customers as suggested by the ratings of 4 and 5 for onboard service.
pd.crosstab(df.Leg_room_service,df.satisfaction, margins=True).style.background_gradient(cmap='summer_r')
Better leg room service will yield better satisfaction among customers as suggested by the ratings of 4 and 5 for leg room service
pd.crosstab(df.Food_and_drink,df.satisfaction, margins=True).style.background_gradient(cmap='summer_r')
Better food and drink will yield better satisfaction among customers as suggested by the ratings of 4 and 5 for Food and drink